There are several directions for extending our work. One important area is to move beyond normal sequence models and consider multiple graphical models. The graph fused lasso method for estimation and structure learning in multiple Gaussian graphical models [1]} has got much attention in recent years. The Horseshoe fusion prior may be utilized in this instance for precision matrix estimation over several groups and structure learning of respective graphical models. Additionally, our proposed approach may be extended to isotonic regression models, where there is a natural ordering in the means of the observations. We propose to explore these ideas as future work.
